6 papers
Comparing Classifiers: A Case Study Using PyCM
Sadra Sabouri, Alireza Zolanvari, Sepand Haghighi
Selecting an optimal classification model requires a robust and comprehensive understanding of the performance of the model. This paper provides a tutorial on the PyCM library, dem…
PyMilo: A Python Library for ML I/O
AmirHosein Rostami, Sepand Haghighi, Sadra Sabouri +1
PyMilo is an open-source Python package that addresses the limitations of existing Machine Learning (ML) model storage formats by providing a transparent, reliable, and safe method…
Wasserstein distributionally robust risk-constrained iterative MPC for motion planning: computationally efficient approximations
Alireza Zolanvari, Ashish Cherukuri
This paper considers a risk-constrained motion planning problem and aims to find the solution combining the concepts of iterative model predictive control (MPC) and data-driven dis…
Iterative risk-constrained model predictive control: A data-driven distributionally robust approach
Alireza Zolanvari, Ashish Cherukuri
This paper proposes an iterative distributionally robust model predictive control (MPC) scheme to solve a risk-constrained infinite-horizon optimal control problem. In each iterati…
Data-driven distributionally robust optimization over a network via distributed semi-infinite programming
Ashish Cherukuri, Alireza Zolanvari, Goran Banjac +1
This paper focuses on solving a data-driven distributionally robust optimization problem over a network of agents. The agents aim to minimize the worst-case expected cost computed…
Data-driven distributionally robust iterative risk-constrained model predictive control
Alireza Zolanvari, Ashish Cherukuri
This paper considers a risk-constrained infinite-horizon optimal control problem and proposes to solve it in an iterative manner. Each iteration of the algorithm generates a trajec…